Most companies that partner with AI consultants have a laundry list of challenges they want to overcome. The sales team wants improved lead scoring. Operations want automated reporting. Customer service is overwhelmed by tickets. Finance needs forecasting tools. Everyone has a project they want tackled, and everyone believes that their request is priority one.
Yet good consultants don’t simply address the shiniest object or what came up first in the kick-off meeting. They have a methodology that considers quick wins, transformation, and the dirty reality of how organizations function. The reason prioritization is crucial is because more people underestimate how project order matters. Often, getting the order of AI implementation wrong will sink the ship entirely rather than bring the organization in for a successful lift.
Step 1: The State of Your Data
Before a consultant begins to consider which business challenge to solve first, they first look at the state of your data. And no, this isn’t sexy AI implementation. But it sets the stage for everything else.
Consultants assess where data lives, how clean it is, and whether anyone pays attention to it at all. They ask whether the CRM is updated regularly or if half the sales team doesn’t use it. They look to see if customer service logs elaborate or if they’re one-word blurbs. They want to know if there’s cross-talk between systems or if everything lives entirely in silos.
Therefore, even the biggest business problem in the world won’t matter if the data supporting it isn’t up to par. Thus, consultants often prioritize where this state of data is already better, no, not great, because it never is, but workable. This is why sometimes the biggest “pain point” in a company gets pushed into phase two or three, because it’s not quite ready yet.
Step 2: Low-Hanging Fruit Creates Momentum
Those AI initiatives that live and die based on organizational buy-in have to be watered and nurtured in a way that makes sense from the start. If the first project takes nine months and provides unmeasurable results, good luck securing budget buy-in and employee support for project two.
As such, many consulting engagements begin with what is colloquially known in the industry as a “quick win,” which is a problem that’s defined well enough with decent enough data and an ability to show results quickly (as compared to months down the line). Getting started with Louder.ai, an ai automation consulting service, generally requires finding opportunities that build value without necessitating extensive organizational change.
For example, maybe it’s simply creating a report that someone spends three hours compiling weekly. Or maybe it’s building a simple chatbot to address the ten most frequently asked customer questions. These aren’t groundbreaking projects, but they validate the technology and make employee buy-in feel valid, as progress is being made by implementing AI into their day-to-day routines.
The psychological impact of an early win cannot be overstated. When employees see that AI is actually making their lives easier instead of just increasing their burdens, resistance lessens. When executives see quantifiable results sooner rather than later, they’re more likely to open their wallets for longer-term, more challenging initiatives.
Step 3: Impact vs Effort
From there, consultants typically prioritize by what amounts to a matrix. On one axis is business impact – how much this will save cost, create revenue, or improve key metrics. On another axis is effort – where the triangle points based on technical complexity required, organizational change required, and time to results.
The center point is high impact with moderate effort; yet rarely do consultants discover perfect options sitting there. More often than not, they’re forced to make trade-offs and have candid conversations about what’s possible.
An initiative may have huge potential impact but require an overhaul of three departments meeting together to get on board and integrate five different systems with 200 employees who need training. That’s not a phase one project; that’s a phase three or four project after the organization has undergone a change management initiative once or twice successfully.
On the other hand, something that’s a no-brainer technical implementation that no one will ever use is just as bad as something impossible to implement. Consultants take the time to assess who will be using the AI tools, not just who will end up paying for them. If there’s no room for a proposed solution within actual workflows, it doesn’t matter how clever it might be.
Step 4: Dependency Mapping
Some projects cannot be solved until other projects are addressed first, and consultants map these dependencies because tackling problems in the wrong order leads to expensive do-overs.
For example, Company A might want improved AI-driven inventory optimization, but if its inventory tracking systems are abysmal at their current state, AI will just make bad decisions faster. Thus, consultants may prioritize fixing baseline inventory problems before bringing in sophisticated forecasting tools.
Or Company B may want comprehensive personalized marketing automation but needs a unified customer database (to do this) with clean entries (but first must remove duplicate entries) which necessitates organized standards of entry for data first. An experienced consultant sees this chain and knows they cannot leap frog to the end.
This also makes sense in terms of organizational readiness; some AI applications require people to trust what comes up as recommendations, and that trust does not exist right away. Therefore, consultants may stagger projects within build trust slowly, first starting with AI tools that help humans make decisions as opposed to AI driven solutions that act on their own.
Step 5: The Unspoken Politics
What doesn’t come up in most case studies? Organizational politics matter, and they matter a lot.
If someone in sales knows the company president well enough and really needs lead scoring, there may be an emphasis on prioritizing this problem despite ROI netting something else more favorable. If two departments have bad blood between them, an experienced consultant might steer clear of projects down the road requiring them to work closely together until relationships mend themselves.
A nuanced approach fosters success here. On the ground level, trained consultants will ask who championed other technological initiatives and if they worked. They’ll observe which departments maintain orderly workflows and which are chaotic. They’ll recognize if leadership jumps through hoops for change management to occur or assumes new solutions are just going to magically change cultural dynamics.
Thus, sometimes the “right” solution from a technical standpoint isn’t right politically – and it’s better to take a less-than-optimal solution from a tech standpoint because it’s got strong internal advocates and ally teams in place so that success comes there and fosters success for more difficult solutions down the road.
Step 6: Thinking Long Game
While it’s important for consultants to focus on quick wins and tangible initial projects, they’re thinking several moves ahead in terms of initial priority.
The logic they apply to determine Problem A needs resolving first over Problem B has value both ways; it’s mitigating risk while fostering the plan toward larger goals over time.
It could be that an initial process results in something AI built for one department, and then things can scale from there thanks to a created template across organizations. Or getting the easy solution now creates a data set for training later down the line to build something vastly more complex.
Consultants allay transparency by presenting their logic for prioritization upfront, explaining why A must come before B while recognizing risks of established workflow through later stages, and are willing to flex upon timing should disruptions occur, even if it’s revealed through that quick win we’ve discussed earlier creating complications previously unseen, or new opportunities arise.
The companies that get the most from partnering with AI consultants are those that best recognize what should come first, even if they have thin budgets and more pedestrian challenges compared to companies with lots of money at their disposal but fail to see how important strategy is for successful implementation through proper assessment of staged resource availability and organizational buy-in determined through change management through each interval before moving onward so that AI solutions avoiding becoming yet another obsolete software license on a forgotten renewal roster.
